1. Linked Environments for Atmospheric Discovery (LEAD): Web Services for Meteorological Research and Education

2. What Would YOU Do if These Were About to Occur?

3. What THEY Do to Us!!! n Each year in the US, mesoscale weather – local floods, tornadoes, hail, strong winds, lightning, and winter storms – causes hundreds of deaths, routinely disrupts transportation and commerce, and results in annual economic losses > $13B.

4. What Weather Technologies Do… Forecast Models NEXRAD Radar Decision Support Systems Virtually Nothing!!!

5. Tornadic Storms Moderate Rain Radars Do Not Adaptively Scan

6. Operational Models Run Largely on Fixed Schedules in Fixed Domains

7. Cyberinfrastructure is Virtually Static ENIAC (1948) ARPANET (1980) Abilene Backbone (2005) Earth Simulator (2005) National Lambda Rail (2005)

8. We Teach Using Current Weather Data But Students Don’t Interact With It

9. So What??? Weather is Local, High-Impact, Heterogeneous and Rapidly Evolving…Yet Our Technologies and Thinking are Static Severe Thunderstorms Fog Rain and Snow Rain and Snow Intense Turbulence Snow and Freezing Rain

10. The Reality for Society: Dynamic, Local and High Impact

11. A Fundamental Research Question A Fundamental Research Question n Can we better understand the atmosphere, educate more effectively about it, and forecast more accurately if we adapt our technologies and approaches to the weather as it occurs? n People, even animals adapt/respond: Why don’t our resources???

12. Sponsored by the National Science Foundation

13. The LEAD Vision Revolutionize the ability of scientists, students, and operational practitioners to observe, analyze, predict, understand, and respond to intense local weather by interacting with it dynamically and adaptively in real time

14. What Does Adaptation Really Mean? What Does it Buy? Charles Darwin

15. Sample Problem: March 2000 Fort Worth Tornadic Storm

16. n Tornado Local TV Station Radar

17. NWS 12-hr Computer Forecast Valid at 6 pm CDT (near tornado time) No Explicit Evidence of Precipitation in North Texas

18. Reality Was Quite Different!

19. LEAD Approach Streaming Observations Storms Forming or Conditions Favorable Forecast Model On-Demand Grid Computing Data Mining

20. 6 pm 7 pm8 pm Radar Xue et al. (2003) Fort Worth

21. 6 pm 7 pm8 pm Radar Fcst With Radar Data 2 hr 3 hr 4 hr Xue et al. (2003) Fort Worth

22. What Does it Take to Make This Possible? n Adaptive weather tools n Adaptive sensors n Adaptive cyberinfrastructure In a User-Centered Framework Where Everything Can Mutually Interact

23. How Does LEAD Do It? The Notion of a Web Service n Web Service: A program that carries out a specific set of operations based upon requests from clients n The LEAD architecture is a “Service Oriented Architecture” (SOA), which means that all of the key functions are represented as a set of services.

24. Service-Oriented Architecture Service A (Analysis) Service B (Model) Service C (Radar Stream) Service D (Work Space) Service E (VO Catalog) Service F (Viz Engine) Service G (Monitoring) Service H (Scheduling) Service I (Decoder) Service J (Repository) Service K (Mining) Service L (Decoder) Many others…

25. Service B (Model) Service A (Analysis) Service C (Radar Stream) Service D (Work Space) Service K (Mining) Service L (Decoder) Service J (Repository) Can Solve Broad Classes of Problems by Linking Services Together in Workflows

26. A LEAD Weather Prediction Workflow

27. Fault Tolerance in Action

28. Back to the Earlier Example…

29. As a Forecaster Worried About This Reality… 7 pm

30. As a Forecaster Worried About This Reality… How Much Trust Would You Place in This Model Forecast? 3 hr 7 pm

31. Actual Radar

32. Ensemble Member #1 Ensemble Member #2 Ensemble Member #3 Ensemble Member #4 Control Forecast Actual Radar

33. Probability of Intense Precipitation n Model Forecast n Radar Observations

34. Hazardous Weather Test Bed n Collaboration with the NOAA Storm Prediction Center and National Severe Storms Laboratory n Mid-April through early June n Goals – to begin understanding… –The fundamental predictability of intense thunderstorms –The utility of ensemble forecasts compared to single fine-grids –Value of on-demand forecasts launched manually and automatically

35. The Value of Adaptation: Forecaster- Initiated Predictions n Brewster et al. (2008) Observed Radar Echoes 20 hr Pre-Scheduled Forecast 5 hr LEAD Dynamic WRF-ARF With Radar Data Assimilation

36. Centers of On-Demand Forecast Grids Launched Automatically at NCSA During 2007 Spring Experiment Launched automatically in response to hazardous weather messages (tornado watches, mesoscale discussions) Launched based on forecaster guidance Graphic Courtesy Jay Alameda and Al Rossi, NCSA

37. Real Impact!

38. Adaptive Observing Systems: Current Operational Radar System in US NEXRAD Doppler Radar Network

39. #2. Earth’s curvature prevents 72% of the atmosphere below 1 km from being observed #1. Operates largely independent of the prevailing weather conditions #3. Operates entirely independent from the models and algorithms that use its data The Limitations of NEXRAD

40. Data Courtesy Brenton MacAloney II, National Weather Service NEXRAD

41. © 1998 Prentice-Hall, Inc. -- From: Lutgens and Tarbuck, The Atmosphere, 7 th Ed. NEXRAD/ MPAR ($$$) 0-3 km CASA ($) NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA)

42. Example of Adaptive Sampling

43. Oklahoma Test Bed

44. NWS Operational (NEXRAD)

45. Experimental (CASA) NWS Operational (NEXRAD)

46. Real Time Testing Today Radar Observations

47. Real Time Testing Today 9-Hour Forecast

50. The Million Dollar Question: Will Computer Models Ever Be Able to Predict Tornadoes?

51. Warn on Explicit Forecast?

52. n Be careful what you wish for! A one-hour model- based “tornado warning” would be a game changer n Social and behavioral science elements are critical –Why did 550 people die in the US last year from tornadoes? n Our ability to effectively warn the public and understand its response is relatively crude n This is an area ripe for additional research – and it is ESSENTIAL for making progress Challenges

53. n Each set of forecasts (ensemble and individual) –produces 6 TB of output PER DAY –Requires 9000 cores (750 nodes) of the Kraken Cray XT5 at Oak Ridge –Takes 6.5 hours to run n Provisioning of data in real time n Management in a repository – retention time? n Experiment reproducibility!! n Creating products that will benefit the public (smart device location-based warnings) Other Challenges

54. LEAD: Potential to Transform Meteorological Research And Education